Goal
Discuss machine learning techniques.
Schedule
| Date | Presenter | Paper | Comments |
|---|---|---|---|
| 1: 27-10-2015 | Alexander Ly | Breiman, L (2001) and Ch 2 | Slides |
| 2: 17-11-2015 | Tahira Jamil | Linear regression (Ch 3) | Slides |
| 3: 01-12-2015 | Lourens Waldorp | Logistic Regression | Slides |
| 4: 12-01-2016 | Johnny van Doorn, Quentin Gronau | Resampling techniques | Slides a, Slides b |
| 5: 26-01-2016 | Lourens Waldorp | Regularisation: Ridge, lasso | Slides |
| 6: 09-02-2016 | Lourens Waldorp | Gaussian graphical models and the graphical lasso | Slides |
| 7: 23-02-2016 | Alexander Ly | Motivating splines | Slides |
| 8: 08-03-2016 | Alexander Ly | Smoothing splines | Slides |
| 9: 29-03-2016 | Riet van Bork | Classification and regression trees | Slides |
| 10: 05-04-2016 | Joost Kruis | Neural networks | Slides |
| 11: 19-04-2016 | Jonas Haslbeck | Bagging | Slides |
| 12: 10-05-2016 | Gilles de Hollander | Machine learning in cognitive neuroscience | Slides |
| 13: 31-05-2016 | Udo Bohm | Support vector machines | Slides |
| 14: 02-11-2016 | Alexander Ly | Recap | Slides |
| 15: 23-11-2016 | Jonas Haslbeck | Clustering | Slides |
| 16: 07-12-2016 | Pia Tio | Dimensionality reduction | Slides |
| 17: 25-01-2017 | Gilles de Hollander | Mixture modelling | Slides |
| 18: 15-02-2017 | Don van den Bergh | Recommender systems | Slides |
| 19: 01-03-2017 | Johnny van Doorn | PageRank | |
| 20: 22-03-2017 | Koen Derks | N-grams | |
| 21: 12-04-2017 | Claire Stevenson | Topic modelling | |
| 22: 26-04-2017 | Quentin Gronau | Topic modelling and Bayesian nonparametrics | |
| 23: 21-02-2018 | Sharon Klinkenberg | Kalman filters | Slides |
| 24: 07-03-2018 | Don van den Bergh | Time series | Slides |
| 25: 14-03-2018 | Udo Bohm | Kriging, interpolation and Gaussian processes | Slides |
Literature
– James, G, Witten, D, Hastie, T, Tibshirani, R (2013). An introduction to statistical learning – with applications in R
– Hastie, T, Tibshirani, R, Friedman, JH (2001), The elements of statistical learning Data mining, inference, and prediction
– Breiman, L (2001). Statistical modeling The two cultures
Suggested reading
1: Alexander Ly – General overview
– Breiman, L (2001). Statistical modeling The two cultures”
– James, G, Witten, D, Hastie, T, Tibshirani, R (2013). An introduction to statistical learning – with applications in R – Ch 1, 2
– Hastie, T, Tibshirani, R, Friedman, JH (2001), The elements of statistical learning Data mining, inference, and prediction – Ch1
2: Tahira Jamil – Linear regression
– James, G, Witten, D, Hastie, T, Tibshirani, R (2013). An introduction to statistical learning – with applications in R – Ch 3
– Hastie, T, Tibshirani, R, Friedman, JH (2001), The elements of statistical learning Data mining, inference, and prediction –
3: Lourens Waldorp – Logistic regression
– James, G, Witten, D, Hastie, T, Tibshirani, R (2013). An introduction to statistical learning – with applications in R – Ch 4
– Hastie, T, Tibshirani, R, Friedman, JH (2001), The elements of statistical learning Data mining, inference, and prediction –
4: Johnny van Doorn – K-fold cross validation and Quentin Gronau – Bootstrapping
– James, G, Witten, D, Hastie, T, Tibshirani, R (2013). An introduction to statistical learning – with applications in R – Ch 5
5: Lourens Waldorp – Regularisation: Ridge, lasso
– James, G, Witten, D, Hastie, T, Tibshirani, R (2013). An introduction to statistical learning – with applications in R – Ch 6
6: Lourens Waldorp – Gaussian graphical models and the graphical lasso
7: Alexander Ly – Motivating: Splines
– James, G, Witten, D, Hastie, T, Tibshirani, R (2013). An introduction to statistical learning – with applications in R – Ch 7
– Hastie, T, Tibshirani, R, Friedman, JH (2001), The elements of statistical learning Data mining, inference, and prediction – Ch 5
9: Riet van Bork – Classification and regression trees
– James, G, Witten, D, Hastie, T, Tibshirani, R (2013). An introduction to statistical learning – with applications in R – Ch 8
– Hastie, T, Tibshirani, R, Friedman, JH (2001), The elements of statistical learning Data mining, inference, and prediction – Ch 9
10: Joost Kruis – Neural networks
– Hastie, T, Tibshirani, R, Friedman, JH (2001), The elements of statistical learning Data mining, inference, and prediction – Ch 11
Jonas Haslbeck – Bagging
– James, G, Witten, D, Hastie, T, Tibshirani, R (2013). An introduction to statistical learning – with applications in R – Ch 8.2
13: Udo Bohm- Suppor vector machines
– James, G, Witten, D, Hastie, T, Tibshirani, R (2013). An introduction to statistical learning – with applications in R – Ch 9
– Hastie, T, Tibshirani, R, Friedman, JH (2001), The elements of statistical learning Data mining, inference, and prediction – Ch 12
15: Jonas Haslbeck – Clustering
– James, G, Witten, D, Hastie, T, Tibshirani, R (2013). An introduction to statistical learning – with applications in R – Ch 10
– Hastie, T, Tibshirani, R, Friedman, JH (2001), The elements of statistical learning Data mining, inference, and prediction – Ch 14
16: Pia Tio – Dimensionality reduction
– James, G, Witten, D, Hastie, T, Tibshirani, R (2013). An introduction to statistical learning – with applications in R – Ch 10
17: Gilles de Hollander – Mixture modelling
– Hastie, T, Tibshirani, R, Friedman, JH (2001), The elements of statistical learning Data mining, inference, and prediction – Section 6.8, 8.5, 13.2.3, 14.3.7,
– Marin, JM, Robert, CP (2014). Bayesian essentials with R – Chapter 6
– Bishop, CM (2006). Pattern recognition and machine learning – Section 2.3.9, 5.6, Chapter 9
– Murphy, KP (2012). Machine learning – A probabilistic perspective – Chapter 11
18: Don van den Bergh – Recommender systems
– Ricci, F, Rokach, L, Shapira, B, Kantor, PB (2010). Recommender Systems Handbook – Ch1
– Gorakala, SK, Usuelli, M (2015). Building a Recommendation System with R- Learn the art of building robust and powerful recommendation engines using R
– https://www.coursera.org/learn/recommender-systems-introduction
19: Johnny van Doorn – PageRank
– Barber, D (2015). Bayesian reasoning and machine learning – Ch 23
– Murphy, KP (2012). Machine learning – A probabilistic perspective – Ch 17
– http://www.ams.org/samplings/feature-column/fcarc-pagerank
– Russell, S, Norvig, P (2009). Artificial intelligence – A modern approach – 3rd – Ch 22.3
20: Koen Derks – Text mining
– https://www.youtube.com/watch?t=25s&v=GTrkTDCyO80&app=desktop
21: Leonie Poelstra – Topic modelling
– http://tidytextmining.com/
22: Quentin Gronau – Topic modelling and Bayesian nonparametrics
– Blei, DM, Ng, AY, Jordan, MI (2003). Latent Dirichlet Allocation
– http://blog.echen.me/2012/03/20/infinite-mixture-models-with-nonparametric-bayes-and-the-dirichlet-process/
– Gershman, SJ, Blei, DM (2012). A tutorial on Bayesian nonparametric models
– Orbanz, P, Teh, YW (2010). Bayesian nonparametric models
23: Sharon Klinkenberg – Kalman filters
– Apolloni, B, Ghosh, A, Alpaslan, F, Jain, LC, Patnaik, S (2005). Machine learning and robot perception, Section 5.1.2 p. 172, Section 7.1.1 p. 266
– Barber, D (2015). Bayesian reasoning and machine learning – Section 24.3
– Murphy, KP (2012). Machine learning – A probabilistic perspective – Chapter 18
– Russell, S, Norvig, P (2009). Artificial intelligence – A modern approach – 3rd, Section 15.4
24: Don van den Bergh – Time series
– Shumway, RH, Stoffer, DS (2016). Time Series Analysis and Its Applications- With R Examples – 4th, Ch 1+2
– Montgomery, DC, Jennings, CL, Kulahci, M (2008). Introduction to Time Series Analysis and Forecasting, Ch 1+2
25: Udo Bohm – Kriging, interpolation and Gaussian processes
– Waller, LA, Gotway, CA (2004). Applied Spatial Statistics for Public Health Data Ch 8
– Chiles, Jean-Paul & Delner, P. (1999). Geostatistics: Modeling Spatial Uncertainty. Wiley. Ch. 3
– Cressie, N. A. C. (1993) Spatial Prediction and Kriging, in Statistics for Spatial Data, Revised Edition, John Wiley & Sons, Inc., Hoboken, NJ. Ch. 3
Videos
– Videos by Trevor Hastie and Rob Tibshirani
– MIT Opencourseware: Artificial Intelligence Videos by Patrick Henry Winston